Timeline for What is the PCA representation of an image?
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Nov 29, 2019 at 22:51 | vote | accept | JeffThompson | ||
| Mar 29, 2018 at 19:48 | comment | added | elliotp | @JeffThompson Updated. Also, you may want to search for the term "eigenface" for more information on using PCA specifically for face images. | |
| Mar 29, 2018 at 16:26 | history | edited | elliotp | CC BY-SA 3.0 | updated according to comments |
| Mar 29, 2018 at 12:48 | comment | added | JeffThompson | Thanks, super helpful. If you update your answer with this info, happy to accept it. | |
| Mar 29, 2018 at 12:42 | comment | added | elliotp | @JeffThompson When compressing an image using PCA, you are saying that each image in your dataset has many pixels, but the images are different from each other in only 100 ways. You find 100 "basis images" that represent a typical image well. The reconstructed image is a linear combination of 100 "basis images", and the 100 numbers in the compressed vector are the coefficients multiplying each "basis image". You really can't understand what each "pixel" in the compressed vector means without also plotting the "basis images". To do this in python, try reshaping the rows of pca.components_ | |
| Mar 29, 2018 at 12:20 | comment | added | JeffThompson | Got it. Can you tell me in simple or metaphorical terms what they represent? You mention "variance of projections" but I'm not familiar with those terms and am trying to get my head around what has been pulled out of the image :) | |
| Mar 29, 2018 at 11:03 | comment | added | elliotp | @JeffThompson The reduced vector is not supposed to have any resemblance to the original images. The grey levels in the reduced vector are not supposed to look anything like adges or contrast or any grey levels in the original images. The order of the "pixels" in the reduced vectors is in decreasing variance of projections of the images onto smaller dimensional vectors, and the different "pixels" are uncorrelated, so their appearance resembles noise. | |
| Mar 29, 2018 at 10:48 | comment | added | JeffThompson | This doesn't quite answer my question. There must be some correlation between the original images and the reduced vector – does it have to do with areas of contrast or edges like in haar detection? Or is it purely mathematical? | |
| Mar 29, 2018 at 7:03 | history | answered | elliotp | CC BY-SA 3.0 |